Updated: May 12, 2026
character
This workflow is designed for LTX 2.3 dual-character dialogue video generation, focusing on stable two-person interaction, controlled left-right positioning, stronger motion continuity, and more polished final video quality. Its main purpose is to help creators turn a single reference image into a cinematic two-character conversation shot, where both characters remain visually consistent and the scene keeps a coherent dialogue rhythm instead of drifting into random motion.
The workflow uses LTX 2.3 as the main video generation backbone, with ltx-2.3-22b-distilled-1.1 as the core checkpoint route. It also uses Gemma-style LTX text encoding, LTX audio VAE routing, LTX video VAE decoding, image-to-video conditioning, NAG enhancement, VBVR I2V LoRA support, latent upscaling, multi-stage sampling, and final video export. This makes it more advanced than a basic I2V workflow because it is built specifically for controlled character interaction rather than simple first-frame animation.
The core strength of this workflow is two-character stability. In many AI video workflows, two-person scenes are difficult because characters may swap positions, merge into each other, lose identity, change clothing, or break the left-right relationship. This workflow is designed to reduce those problems by using the input image as a strong visual anchor, then reinforcing the generation with LTXVImgToVideoConditionOnly, LTXVConditioning, and a structured prompt. The negative prompt also directly suppresses subtitles, scene cuts, glitches, warping, extra limbs, extra hands, static frames, low-quality motion, and unwanted transitions.
Another important feature is the VBVR I2V LoRA route. The workflow loads an LTX 2.3 VBVR I2V LoRA, which helps strengthen image-to-video behavior, motion consistency, and prompt adherence. This is especially useful for dialogue-style videos, where small gestures, facial direction, eye contact, and body positioning matter more than large chaotic movement.
The workflow also includes NAG enhancement. NAG is used to improve guidance stability and reduce generation drift during sampling. For dual-character dialogue scenes, this matters because the video must preserve not only the scene style, but also the relationship between the two characters. The left character should remain on the left, the right character should remain on the right, and both should continue facing or reacting to each other in a believable way.
The generation pipeline is also multi-stage. It first builds the initial LTX video latent from the input image, audio latent structure, prompt conditioning, and sampler route. Then it uses LTXVLatentUpsampler and additional refinement sampling stages to improve detail, texture, and visual polish. Instead of exporting a rough first pass, the workflow pushes the video through several controlled refinement steps, giving the final result a cleaner and more cinematic finish.
This workflow is suitable for AI short dramas, anime-style character dialogue, fantasy conversation scenes, virtual influencer interactions, two-person storytelling, product dialogue clips, roleplay videos, YouTube demos, Bilibili tutorials, RunningHub publishing, and Civitai workflow showcases. It is especially useful when you want a two-character shot that feels like a continuous scene rather than disconnected AI motion.
If you want to see how the input image is prepared, how VBVR and NAG improve two-person stability, how the three-stage refinement route works, and how the final enhanced dialogue video is exported, watch the full tutorial from the YouTube link above.
⚙️ Try the Workflow Online
👉 Workflow: https://www.runninghub.ai/post/2048727968108781570/?inviteCode=rh-v1111
Open the link above to run the workflow directly online and view the generation results in real time.
If the results meet your expectations, you can also deploy it locally for further customization.
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📺 Bilibili Updates (Mainland China & Asia-Pacific)
If you are in Mainland China or the Asia-Pacific region, you can watch the video below for workflow demos and a detailed creative breakdown.
📺 Bilibili Video: https://www.bilibili.com/video/BV1DT9zBbEZu/
I will continue updating model resources on Quark Drive:
👉 https://pan.quark.cn/s/20c6f6f8d87b
These resources are mainly prepared for local users, making creation and learning more convenient.
⚙️ 在线体验工作流
👉 工作流： https://www.runninghub.ai/post/2048727968108781570/?inviteCode=rh-v1111
打开上方链接即可直接运行该工作流，实时查看生成效果。
如果觉得效果理想，你也可以在本地进行自定义部署。
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📺 Bilibili 更新（中国大陆及南亚太地区）
如果你在中国大陆或南亚太地区，可以通过下方视频查看该工作流的实测效果与构思讲解。
📺 B站视频： https://www.bilibili.com/video/BV1DT9zBbEZu/
我会在 夸克网盘 持续更新模型资源：
👉 https://pan.quark.cn/s/20c6f6f8d87b
这些资源主要面向本地用户，方便进行创作与学习。